Check the entry on [[Data_distribution#Non-normal_distributions|Non-normal distributions]] to learn more.\n\n\n==== Generalised Linear Models ====\n'''You have arrived at a Generalised Linear Model (GLM).''' GLMs are a family of models that are a generalization of ordinary linear regressions. The key R command is <code>glm()</code>.\n\nDepending on the existence of random variables, there is a distinction between Mixed Effect Models, and Generalised Linear Models based on regressions.\n\n<imagemap>Image:Statistics Flowchart - GLM random variables.png|650px|center|\npoly 289 4 153 141 289 270 422 137 [[An_initial_path_towards_statistical_analysis#Generalised_Linear_Models]]\npoly 139 151 3 288 139 417 272 284 [[Mixed Effect Models]]\npoly 439 149 303 286 439 415 572 282 [[Generalized Linear style="width: 33%"| \'\'\'[[:Category:Past|Past]]\'\'\' || style="width: 33%"| \'\'\'[[:Category:Present|Present]]\'\'\' || \'\'\'[[:Category:Future|Future]]\'\'\'\n|}\n<br/>__NOTOC__\n<br/><br/>\n\'\'\'In short:\'\'\' Generalized Linear Models (GLM) are a family of models that are a generalization of [[Regression Analysis|ordinary linear regression]], thereby allowing for different statistical distributions to be implemented.\n\n\n== Background ==\n[[File:GLM.png|400px|thumb|right|\'\'\'SCOPUS hits per year for Generalized Linear Models until 2020.\'\'\' Search terms: \'Generalized Linear Model\' in Title, Abstract, Keywords. Source: own.]]\nBeing baffled by the restrictions of regressions that rely on the normal distribution, John Nelder and Robert Wedderburn developed Generalized Linear Models (GLMs) in the 1960s to encompass different statistical 269 423 138 [[Data distribution]]\npoly 138 151 2 287 136 416 270 284 271 285 [[An_initial_path_towards_statistical_analysis#Generalised_Linear_Models|Generalised Linear Models]]\npoly 440 152 304 288 438 417 572 285 573 286 [[An_initial_path_towards_statistical_analysis#Generalised_Linear_Models|Generalised Linear Models]]\n</imagemap>\n\n'''How do I know?'''\n* Try to understand the data type of your dependent variable and what it is measuring. For example, if your data is the answer to a yes/no (1/0) question, you should apply a GLM with a Binomial distribution. If it is count data (1, 2, 3, 4...), use a Poisson Distribution.\n* Check the entry on [[Data_distribution#Non-normal_distributions|Non-normal distributions]] to learn